Precision and RecallThe Tradeoff... Again
To wrap everything up, let's look back at our AI doctor dataset, this time exploring the intense tug-of-war between all four evaluation metrics you've learned: accuracy, precision, recall, and the F1-score.
Instead of just talking about the tradeoff between these metrics, let's visualize it! The chart below plots out the exact score each metric achieves as we slowly raise the classification threshold from 0% all the way up to 100%. Watch how shifting that decision boundary completely alters what the AI prioritizes.
Visualizing the Balance
The chart above proves that the tradeoff between precision and recall is very much alive.
At lower decision thresholds, the AI acts paranoid: it catches every single case (perfect recall), but causes tons of false alarms (awful precision). As we raise the threshold, the AI becomes more conservative, and precision skyrockets while recall drops off.
As you might expect from our previous section, the F1-score hits its absolute maximum peak only when the values of precision and recall are
One final takeaway: Look at the line for accuracy. It barely changes even when the threshold is drastically adjusted. This stagnant behavior is the ultimate proof that accuracy was a dangerously poor metric for evaluating this problem from the very start!